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Identifying Best Target Pocket for Small Molecule Inhibition of Beta Catenin/E‐Cadherin Binding
Author(s) -
Khan Faiha Rafia,
Zhang Di,
Koes David
Publication year - 2020
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2020.34.s1.08811
Subject(s) - druggability , catenin , wnt signaling pathway , beta catenin , cadherin , beta (programming language) , small molecule , chemistry , microbiology and biotechnology , computational biology , biochemistry , cell , signal transduction , biology , gene , computer science , programming language
The Wnt/beta catenin signaling pathway has been linked to the growth of certain types of cancer cells secondary to an uncontrolled increased expression of beta catenin. Finding a molecule that can inhibit the activity of beta catenin and reduce its concentration in cells could treat cancers involving this pathway. E‐cadherin is a protein involved in maintaining the integrity and adhesiveness of a cell. Disrupting the interaction between E‐cadherin and beta catenin may inhibit beta catenin expression, which may reduce the survivability of the cancer cell. In this study, we aimed to find the best target pocket for beta catenin/E‐cadherin binding based on large volume and high “druggability” score. The “druggability” score measures the binding potential of small molecules to the pocket of interest. We analyze the pocket of interest in both the monomer (beta catenin) and the complex (beta catenin with E‐cadherin bound) in order to identify the best target for small molecules. We hypothesize that the pocket volume and “druggability” score will increase when comparing the pocket of interest in the monomer versus the complex. First, we used the software PyMol and PocketQuery to search for key residues in the E‐cadherin and beta catenin interaction. The residues chosen were the amino acids Leucine 661 and Aspartate 665. Then, we used the website Pharmit to add these residues as features to the beta catenin receptor to screen for potential molecules that may bind using these residues. The pocket that these molecules were bound to was the pocket we chose to focus on. In order to eventually obtain more potential molecules, we ran a pocket volume analysis using the software MDPocket to see if the pocket volume fluctuates over time in both the monomer and the complex. We also used the software Fpocket to calculate a “druggability score” for both the monomer and the complex as the more “druggable” a pocket is, the better target it is. The results showed that the volume fluctuates over time in the monomer and the complex, with the complex having overall larger volumes. Furthermore, the highest “druggability” scores from a scale of −1.0 (“undruggable”) to +1.0 (most “druggable”) in the monomer and complex were 0.601 and 0.984, respectively. With these results, we conclude that the complex may be a better target for future analysis as the properties of having both a larger pocket volume and a higher “druggability” score correlates with an increased chance for a small molecule to bind.